Avionics systems located on aircraft can be used to determine optimal or enhanced operating states of the aircraft based on various operating conditions and other parameters. For instance, data indicative of engine operating modes, flight path information, engine parameters (e.g., throttle setting, fuel flow, etc.), altitude, trim conditions, weight, and other operating parameters can be used to determine operating state variables, such as speed and/or altitude of an aircraft, to reduce the cost of conducting a flight. The cost of a flight can be defined, for instance, in terms of fuel consumption and/or time to achieve a flight range associated with the flight. The aircraft can be controlled in accordance with the determined operating variables to increase efficiency.
Due to the complexity of the optimization algorithms used to determine the enhanced operating states of the aircraft, it can be difficult to perform the calculations in real time using a computing system located onboard an aircraft. For instance, one example optimization algorithm can include reducing an objective function through an iterative process. Each iteration can require function evaluation. In some cases, solving for aircraft trim state can be required to perform the optimization. Solving for aircraft trim state can itself be an iterative computation that is very computationally expensive. Nesting the iterations to solve for aircraft trim and to perform the optimization can increase the computation time by orders of magnitude and can be impractical to perform using onboard avionics systems.
Some approaches to aircraft performance optimization include performing the calculations a priori in an offline (not in the embedded avionics system) computational environment and tabulating the results. During flight, the avionics system can access the tabulated results and look up a desired operating state (e.g., speed and altitude) based on an operating point of the aircraft (e.g., based on a current altitude and weight of the aircraft). A drawback of this approach is that assumptions must be made for certain operating parameters, such as engine parameters and flight path parameters, as opposed to using real time measurements of actual operating parameters during flight. This can cause potential inaccuracies in the operating state determined by the optimization algorithm, leading to increased costs (e.g., in terms of fuel consumption and/or flight time) for a flight.